SoroushVahidi/scidocs huggingface dataset:
An Efficient Synchronization Mechanism for Mirrored Game Architectures
(2004)

Existing online multiplayer games typically use a client-server model, which introduces added latency as well as a single bottleneck and single point of failure to the game. Distributed multiplayer games minimize latency and remove the bottleneck, but require special synchronization mechanisms to provide a consistent game for all players. Current synchronization methods have been borrowed from distributed military simulations and are not optimized for the requirements of fast-paced multiplayer games. In this paper we present a new synchronization mechanism, trailing state synchronization (TSS), which is designed around the requirements of distributed first-person shooter games. We look at TSS in the environment of a mirrored game architecture, which is a hybrid between traditional centralized architectures and the more scalable peer-to-peer architectures. Mirrored architectures allow for improved performance compared to client-server architectures while at the same time allowing for a greater degree of centralized administration than peer-to-peer architectures. We evaluate the performance of TSS and other synchronization methods through simulation and examine heuristics for selecting the synchronization delays needed for TSS.
Algorithmic Design of Low-Power Variable-Stiffness Mechanisms
(2017)

Compliant actuators enabling low-power stiffness adaptation are missing ingredients and key enablers of next generation robotic systems. One of the key components of these actuators is the mechanism implementing stiffness adaptation that requires sophisticated control and nontrivial mechanical design. However, despite recent advances in controlling these systems, their design remains experience based and not well understood. In this paper, we present an optimization-based computational framework for the design of intrinsically low-power compliant variable stiffness mechanisms. The core ingredient of this framework is the mathematical formulation of the design problem—provided by a constrained nonlinear parameter optimization—which is computationally solved here to identify optimal variable stiffness designs. We show the basic capability of this formulation in finding parameters for variable stiffness mechanisms that require the least power by design. Further, we demonstrate the generality of this method in cross-comparing mechanisms with different kinematic topology to identify the one that requires the least power by design.
Computer- vs. paper-based tasks: are they equivalent?
(2008)

In 1992, Dillon published his critical review of the empirical literature on reading from paper vs. screen. However, the debate concerning the equivalence of computer- and paper-based tasks continues, especially with the growing interest in online assessment. The current paper reviews the literature over the last 15 years and contrasts the results of these more recent studies with Dillon's findings. It is concluded that total equivalence is not possible to achieve, although developments in computer technology, more sophisticated comparative measures and more positive user attitudes have resulted in a continuing move towards achieving this goal. Many paper-based tasks used for assessment or evaluation have been transferred directly onto computers with little regard for any implications. This paper considers equivalence issues between the media by reviewing performance measures. While equivalence seems impossible, the importance of any differences appears specific to the task and required outcomes.
Cellular commitment in the developing cerebellum
(2014)

The mammalian cerebellum is located in the posterior cranial fossa and is critical for motor coordination and non-motor functions including cognitive and emotional processes. The anatomical structure of cerebellum is distinct with a three-layered cortex. During development, neurogenesis and fate decisions of cerebellar primordium cells are orchestrated through tightly controlled molecular events involving multiple genetic pathways. In this review, we will highlight the anatomical structure of human and mouse cerebellum, the cellular composition of developing cerebellum, and the underlying gene expression programs involved in cell fate commitments in the cerebellum. A critical evaluation of the cell death literature suggests that apoptosis occurs in ~5% of cerebellar cells, most shortly after mitosis. Apoptosis and cellular autophagy likely play significant roles in cerebellar development, we provide a comprehensive discussion of their role in cerebellar development and organization. We also address the possible function of unfolded protein response in regulation of cerebellar neurogenesis. We discuss recent advancements in understanding the epigenetic signature of cerebellar compartments and possible connections between DNA methylation, microRNAs and cerebellar neurodegeneration. Finally, we discuss genetic diseases associated with cerebellar dysfunction and their role in the aging cerebellum.
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
(2016)

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network (N3) that learns to map a single image to a state in a Newtonian scenario. Our evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes more than 6000 videos aligned with Newtonian scenarios represented using game engines, and more than 4500 still images with their ground truth dynamics.
Neural networks in building QSAR models.
(2006)

This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resulting models, the issues of generalization and memorization, the problems of overfitting and overtraining, the learning dynamics, regularization, and the use of neural network ensembles. The next part of the chapter focuses attention on the use of descriptors. It reviews different descriptor selection and preprocessing techniques; considers the use of the substituent, substructural, and superstructural descriptors in building common QSAR models; the use of molecular field descriptors in three-dimensional QSAR studies; along with the prospects of "direct" graph-based QSAR analysis. The chapter starts with a short historical survey of the main milestones in this area.
Complexity of vehicle routing and scheduling problems
(1981)

The complexity of a class of vehicle routing and scheduling problems is investigated. We review known NP-hardness results and compile the results on the worst-case performance of approximation algorithms. Some directions for future research are suggested. The presentation is based on two discussion sessions during the Workshop to Investigate Future Directions in Routing and Scheduling of Vehicles and Crews, held at the University of Maryland at College Park, June 4-6, 1979.
Flat-Region Detection and False Contour Removal in the Digital TV Display
(2005)

Bit-depth reduction in digital displays results in false contours in the image. Moreover some of video enhancement processing in the digital TV display, such as histogram equalization, contrast enhancement, and increasing sharpness, etc., make false contours more visible. Bit-depth reduction comes from various display limitations such as video memory constraints, physical characteristics of the display, display drivers, and coarse MPEG quantization, etc. Daly, SJ et al., (2004). We present an efficient method for detecting and segmenting flat-region in the image, and a technique for bit-depth extension to effectively remove false contours. We have simulated bit-depth reduction followed by video enhancement processing that cause false contours with various video image sequences including simple patterns. Our result shows that false contours are effectively removed in the flat-region in the image while the sharpness of object edges is preserved
BakerSFIeld : Bringing software fault isolation to x 64
(2014)

We present BakerSFIeld, a Software Fault Isolation (SFI) framework for the x64 instruction set architecture. BakerSFIeld is derived from PittSFIeld, and retains the assembly language rewriting style of sandboxing, but has been substantially re-architected for greater clarity, separating the concerns of rewriting the program for memory safety, and re-aligning the code for control-flow safety. BakerSFIeld also corrects several significant security flaws we found in PittSFIeld. We measure the performance of BakerSFIeld on a modern 64-bit desktop machine and determine that it has significantly lower overhead than its 32-bit cousin.
Leveraging Multiple Domains for Sentiment Classification
(2016)

Sentiment classification becomes more and more important with the rapid growth of usergenerated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent and training sentiment classifier for every domain is inefficient and often impractical; second, since the quantity of labeled data is important for assessing the quality of classifier, it is hard to evaluate classifiers when labeled data is limited for certain domains. To address the challenges mentioned above, we focus on learning high-level features that are able to generalize across domains, so a global classifier can benefit with a simple combination of documents from multiple domains. In this paper, the proposed model incorporates both labeled and unlabeled data from multiple domains and learns new feature representations. Our model doesn’t require labels from every domain, which means the learned feature representation can be generalized for sentiment domain adaptation. In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features. Empirical evaluations demonstrate our model outperforms baselines and yields competitive results to other state-of-the-art works on the benchmark dataset.
Factors that relate to good and poor handwriting.
(1996)

OBJECTIVE
This study investigated the relationships between specific performance components, eye-hand coordination, visuomotor integration, in-hand manipulation, and handwriting skill.


METHOD
A sample of 48 typical first grade students were identified as good and poor handwriters by their teachers. Each child completed the Motor Accuracy Test; the Developmental Test of Visual-Motor Integration (VMI); two tests of in-hand manipulation, including a rotation and a translation task; and the Minnesota Handwriting Test (MHT).


RESULTS
All test scores for the subjects with good handwriting were significantly higher than those of the subjects with poor handwriting. Each performance component test was significantly correlated to MHT scores. Translation, VMI, and rotation scores were significant predictors of MHT scores, accounting for almost 73% of variance. A discriminant analysis using the performance components correctly classified 98% of the students as good or poor handwriters.


CONCLUSION
In-hand manipulation has significant association to handwriting skill.
Cloud Log Forensics: Foundations, State of the Art, and Future Directions
(2016)

Cloud log forensics (CLF) mitigates the investigation process by identifying the malicious behavior of attackers through profound cloud log analysis. However, the accessibility attributes of cloud logs obstruct accomplishment of the goal to investigate cloud logs for various susceptibilities. Accessibility involves the issues of cloud log access, selection of proper cloud log file, cloud log data integrity, and trustworthiness of cloud logs. Therefore, forensic investigators of cloud log files are dependent on cloud service providers (CSPs) to get access of different cloud logs. Accessing cloud logs from outside the cloud without depending on the CSP is a challenging research area, whereas the increase in cloud attacks has increased the need for CLF to investigate the malicious activities of attackers. This paper reviews the state of the art of CLF and highlights different challenges and issues involved in investigating cloud log data. The logging mode, the importance of CLF, and cloud log-as-a-service are introduced. Moreover, case studies related to CLF are explained to highlight the practical implementation of cloud log investigation for analyzing malicious behaviors. The CLF security requirements, vulnerability points, and challenges are identified to tolerate different cloud log susceptibilities. We identify and introduce challenges and future directions to highlight open research areas of CLF for motivating investigators, academicians, and researchers to investigate them.
Relations among positive parenting, children's effortful control, and externalizing problems: a three-wave longitudinal study.
(2005)

In a 3-wave longitudinal study (with assessments 2 years apart) involving 186 early adolescents (M ages of approximately 9.3, 11.4, and 13.4), the hypothesis that parental warmth/positive expressivity predicts children's effortful control (EC) (a temperamental characteristic contributing to emotion regulation) 2 years later, which in turn predicts low levels of externalizing problems another 2 years later, was examined. The hypothesis that children's EC predicts parenting over time was also examined. Parents were observed interacting with their children; parents and teachers reported children's EC and externalizing problems; and children's persistence was assessed behaviorally. Children's EC mediated the relation between positive parenting and low levels of externalizing problems (whereas there was no evidence that children's EC predicted parenting).
Uninvited Connections: A Study of Vulnerable Devices on the Internet of Things (IoT)
(2014)

The Internet of Things (IoT) continues to grow as uniquely identifiable objects are added to the internet. The addition of these devices, and their remote connectivity, has brought a new level of efficiency into our lives. However, the security of these devices has come into question. While many may be secure, the sheer number creates an environment where even a small percentage of insecure devices may create significant vulnerabilities. This paper evaluates some of the emerging vulnerabilities that exist and puts some figures to the scale of the threat.
Adaptive Current Control for Grid-Connected Converters With LCL Filter
(2009)

This paper presents a discrete-time adaptive current controller for grid-connected pulse width modulation voltage source converters with LCL filter. The main attribute of the proposed current controller is that, in steady state, the damping of the LCL resonance does not depend on the grid characteristic since the adaptive feedback gains ensure a predefined behavior for the closed-loop current control. An overview of model reference adaptive state feedback theory is presented aiming to give the reader the required background for the adaptive current control design. The digital implementation delay is included in the model, and the stability concerning the variation of the grid parameters is analyzed in detail. Furthermore, current distortions due to the grid background voltage are rejected without using the conventional stationary resonant controllers or the synchronous proportional-plus-integral controllers. Simulation and experimental results are presented to validate the analysis and to demonstrate the good performance of the proposed controller for grid-connected converters subjected to large grid impedance variation and grid voltage disturbances.
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
(2017)

Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domainspecific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.
What is Your Organization 'Like'?: A Study of Liking Activity in the Enterprise
(2016)

The 'like' button, introduced by Facebook several years ago, has become one of the most prominent icons of social media. Similarly to other popular social media features on the web, enterprises have also recently adopted it. In this paper, we present a first comprehensive study of liking activity in the enterprise. We studied the logs of an enterprise social media platform within a large global organization along a period of seven months, in which 393,720 'likes' were performed. In addition, we conducted a survey of 571 users of the platform's 'like' button. Our evaluation combines quantitative and qualitative analysis to inspect what employees like, why they use the 'like' button, and to whom they give their 'likes'.
Time series contextual anomaly detection for detecting market manipulation in stock market
(2015)

Anomaly detection in time series is one of the fundamental issues in data mining that addresses various problems in different domains such as intrusion detection in computer networks, irregularity detection in healthcare sensory data and fraud detection in insurance or securities. Although, there has been extensive work on anomaly detection, majority of the techniques look for individual objects that are different from normal objects but do not take the temporal aspect of data into consideration. We are particularly interested in contextual outlier detection methods for time series that are applicable to fraud detection in securities. This has significant impacts on national and international securities markets. In this paper, we propose a prediction-based Contextual Anomaly Detection (CAD) method for complex time series that are not described through deterministic models. The proposed method improves the recall from 7% to 33% compared to kNN and Random Walk without compromising the precision.
Sketching Interfaces: Toward More Human Interface Design
(2001)

A s computers grow more powerful, less expensive , and more widely available, people are expecting them not only to perform obvious computational tasks, but also to assist in people-oriented tasks, such as writing , drawing, and designing. This shift is causing some user-interface (UI) researchers to rethink the traditional reliance on methods that are more machine-oriented and to look at ways to support properties like ambiguity, creativity, and informal communication. The idea is to bend computers to people's way of interacting , not the other way around. This flexibility is particularly important in the early stages of UI design itself, when designers need the freedom to sketch rough design ideas quickly, the ability to test designs by interacting with them, and the flexibility to fill in the design details as they make choices. 1 Tools at this stage must support conceptual design, which is characterized by ambiguity and the need to create several design variations quickly, as the " Why Sketching Is Important " sidebar describes. Unfortunately , with current UI tools, designers tend to focus on issues such as colors, fonts, and alignment, which are more appropriate later in the design. Thus, most UI designers resort to sketching ideas on paper, but these are hard to edit and inconvenient for user evaluations. (CMU) have designed, implemented, and evaluated SILK (Sketching Interfaces Like Krazy), an informal sketching tool that combines many of the benefits of paper-based sketching with the merits of current electronic tools. With SILK, designers can quickly sketch an interface using an electronic pad and stylus, and SILK recognizes widgets and other interface elements as the designer draws them. Unlike paper-based sketching, however, designers can exercise these elements in their sketchy state. For example, a sketched scrollbar is likely to contain an elevator or thumbnail, the small rectangle a user drags with a mouse. In a paper sketch, the elevator would just sit there, but in a SILK sketch, designers can drag it up and down, which lets them test the components' or widgets' behavior. SILK also supports the creation of storyboards—the arrangement of sketches to show how design elements behave, such as how a dialog box appears when the user activates a button. Storyboards are important because they give designers a way to show colleagues, customers, or end users early on how an interface will behave. Designers can test the interface at any point, not just …
Interactive multiresolution mesh editing
(1997)

We describe a multiresolution representation for meshes based on subdivision, which is a natural extension of the existing patch-based surface representations. Combining subdivision and the smoothing algorithms of Taubin [26] allows us to construct a set of algorithms for interactive multiresolution editing of complex hierarchical meshes of arbitrary topology. The simplicity of the underlying algorithms for refinement and coarsification enables us to make them local and adaptive, thereby considerably improving their efficiency. We have built a scalable interactive multiresolution editing system based on such algorithms.
Suede: a Wizard of Oz prototyping tool for speech user interfaces
(2000)

Speech-based user interfaces are growing in popularity. Unfortunately, the technology expertise required to build speech UIs precludes many individuals from participating in the speech interface design process. Furthermore, the time and knowledge costs of building even simple speech systems make it difficult for designers to iteratively design speech UIs. SUEDE, the speech interface prototyping tool we describe in this paper, allows designers to rapidly create prompt/response speech interfaces. It offers an electronically supported Wizard of Oz (WOz) technique that captures test data, allowing designers to analyze the interface after testing. This informal tool enables speech user interface designers, even non-experts, to quickly create, test, and analyze speech user interface prototypes.
Ambiguous Intentions: A Paper-like Interface for Creative Design
(1996)

Interfaces for conceptual and creative design should recognize and interpret drawings. They should also capture users’ intended ambiguity, vagueness, and imprecision and convey these qualities visually and through interactive behavior. Freehand drawing can provide this information and it is a natural input mode for design. We describe a pen-based interface that acquires information about ambiguity and precision from freehand input, represents it internally, and echoes it to users visually and through constraint based